

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase     : Any       =						logging.get_logger()



@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Tuple					=   4_2
  __lowerCamelCase			:      Optional[Any]					=   field(default_factory=snake_case				)
  __lowerCamelCase			:      List[str]					=   field(default_factory=snake_case				)





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		str        =		len(list(m.modules()    )    ) == 1 or isinstance(SCREAMING_SNAKE_CASE_   ,nn.Convad    ) or isinstance(SCREAMING_SNAKE_CASE_   ,nn.BatchNormad    )
      if has_not_submodules:
          self.traced.append(SCREAMING_SNAKE_CASE_    )





  def __call__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook    )    )
      self.module(SCREAMING_SNAKE_CASE_    )
      [x.remove() for x in self.handles]
      return self





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda SCREAMING_SNAKE_CASE_    : len(list(x.state_dict().keys()    )    ) > 0   ,self.traced    )    )







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Tuple					=   4_2
  __lowerCamelCase			:      Any					=   4_2
  __lowerCamelCase			:      Tuple					=   0
  __lowerCamelCase			:      List[str]					=   field(default_factory=snake_case				)
  __lowerCamelCase			:      str					=   field(default_factory=snake_case				)





  def __call__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		Tracker(self.dest    )(SCREAMING_SNAKE_CASE_    ).parametrized
      snake_case							:		str        =		Tracker(self.src    )(SCREAMING_SNAKE_CASE_    ).parametrized

      snake_case							:		Any        =		list(filter(lambda SCREAMING_SNAKE_CASE_    : type(SCREAMING_SNAKE_CASE_    ) not in self.src_skip   ,SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Optional[int]        =		list(filter(lambda SCREAMING_SNAKE_CASE_    : type(SCREAMING_SNAKE_CASE_    ) not in self.dest_skip   ,SCREAMING_SNAKE_CASE_    )    )

      if len(SCREAMING_SNAKE_CASE_    ) != len(SCREAMING_SNAKE_CASE_    ):
          raise Exception(
              F"""Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE_    )} operations while"""
              F""" destination module has {len(SCREAMING_SNAKE_CASE_    )}."""    )

      for dest_m, src_m in zip(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):
          dest_m.load_state_dict(src_m.state_dict()    )
          if self.verbose == 1:
              print(F"""Transfered from={src_m} to={dest_m}"""    )







def       lowercase					(	__A :					str ,    __A :					ResNetConfig ,    __A :					Path ,    __A :					bool = True		)				->			Any:





    '''simple docstring'''




    print(f"""Converting {name}..."""		)
    with torch.no_grad():
        snake_case							:		List[str]        =		timm.create_model(a_ ,    pretrained=a_		).eval()
        snake_case							:		List[Any]        =		ResNetForImageClassification(a_		).eval()
        snake_case							:		List[Any]        =		ModuleTransfer(src=a_ ,    dest=a_		)
        snake_case							:		str        =		torch.randn((1, 3, 224, 224)		)
        module_transfer(a_		)

    assert torch.allclose(from_model(a_		) ,    our_model(a_		).logits		), "The model logits don't match the original one."

    snake_case							:		Tuple        =		f"""resnet{"-".join(name.split("resnet"		)		)}"""
    print(a_		)

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name ,    commit_message="""Add model""" ,    use_temp_dir=a_ ,    )

        # we can use the convnext one
        snake_case							:		List[Any]        =		AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""		)
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name ,    commit_message="""Add image processor""" ,    use_temp_dir=a_ ,    )

        print(f"""Pushed {checkpoint_name}"""		)







def       lowercase					(	__A :					Path ,    __A :					str = None ,    __A :					bool = True		)				->			Optional[int]:





    '''simple docstring'''




    snake_case							:		Any        =		"""imagenet-1k-id2label.json"""
    snake_case							:		int        =		1000
    snake_case							:		Union[str, Any]        =		(1, num_labels)

    snake_case							:		Optional[Any]        =		"""huggingface/label-files"""
    snake_case							:		int        =		num_labels
    snake_case							:		str        =		json.load(open(hf_hub_download(a_ ,    a_ ,    repo_type="""dataset"""		) ,    """r"""		)		)
    snake_case							:		Union[str, Any]        =		{int(a_		): v for k, v in idalabel.items()}

    snake_case							:		Any        =		idalabel
    snake_case							:		Tuple        =		{v: k for k, v in idalabel.items()}

    snake_case							:		str        =		partial(a_ ,    num_labels=a_ ,    idalabel=a_ ,    labelaid=a_		)

    snake_case							:		List[str]        =		{
        """resnet18""": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2] ,    hidden_sizes=[64, 128, 256, 512] ,    layer_type="""basic"""		),
        """resnet26""": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2] ,    hidden_sizes=[256, 512, 1024, 2048] ,    layer_type="""bottleneck"""		),
        """resnet34""": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3] ,    hidden_sizes=[64, 128, 256, 512] ,    layer_type="""basic"""		),
        """resnet50""": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3] ,    hidden_sizes=[256, 512, 1024, 2048] ,    layer_type="""bottleneck"""		),
        """resnet101""": ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3] ,    hidden_sizes=[256, 512, 1024, 2048] ,    layer_type="""bottleneck"""		),
        """resnet152""": ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3] ,    hidden_sizes=[256, 512, 1024, 2048] ,    layer_type="""bottleneck"""		),
    }

    if model_name:
        convert_weight_and_push(a_ ,    names_to_config[model_name] ,    a_ ,    a_		)
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(a_ ,    a_ ,    a_ ,    a_		)
    return config, expected_shape


if __name__ == "__main__":
     __lowercase     : Union[str, Any]       =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default=None,
         type=str,
         help=(
             '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
             ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=Path,
         required=True,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         default=True,
         type=bool,
         required=False,
         help='''If True, push model and image processor to the hub.''',
     )

     __lowercase     : Tuple       =						parser.parse_args()
     __lowercase     : Union[str, Any]       =						args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''


# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Union[str, Any]					,       a_						:				Dict      ):
  __a								=       {
      'en': 'Machine learning is great, isn\'t it?',
      'ru': 'Машинное обучение - это здорово, не так ли?',
      'de': 'Maschinelles Lernen ist großartig, oder?',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  __a								=       {
      'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
      'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
      'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
      'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
  }
  __a								=       f"{src_lang}-{tgt_lang}"

  __a								=       f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
  os.makedirs(a_					,       exist_ok=a_      )
  __a								=       os.path.join(a_					,       'README.md'      )
  print(f"Generating {path}"      )
  with open(a_					,       'w'					,       encoding='utf-8'      ) as f:
    f.write(a_      )


# make sure we are under the root of the project
UpperCAmelCase_					=   Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_					=   repo_dir / "model_cards"

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_					=   model_name.split("-")
  UpperCAmelCase_					=   model_cards_dir / "facebook" / model_name
  write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)



